Congratulations to Enrico Capobianco, PhD, Lead Senior Bioinformatics Scientist for CCS’s Computational Biology & Bioinformatics program, on receiving two grants. One NIH award with Dr. Jianning Wei of FAU, and the other an NSF award with Dr. Jun Deng of Yale University.

1. Dynamic Network Analysis of Huntingtin Interactome in Response to Cellular Stresses

Funder	National Institute of Neurological Disorders and Stroke (NINDS)
NIH Grant #	R21NS111202
Investigators
PI	Jianning Wei, PhD | Florida Atlantic University
Co-PI	Enrico Capobianco, PhD | University of Miami

 

Abstract

Neurons are selectively venerable with a low stressor-threshold in neurodegenerative diseases. At molecular levels, responses to cellular stresses are mediated by dynamic protein-protein interactions (PPI). Developing in-depth, dynamic PPI networks is therefore crucial to understand the pathogenesis of these diseases.

Huntington’s disease (HD) is an inherited fatal neurodegenerative disorder caused by a mutation in the huntingtin (htt) gene. It is strongly suggested that Htt serves as a scaffold protein interacting with multiple protein complexes that are involved in diverse cellular functions. Our long-range goal is to understand the molecular functions of Htt and muHtt at various biological states.

The objective of this multi- PI proposal is to map Htt/muHtt interactome under the proteotoxic stress. Our central hypothesis is that normal Htt remodels its interactome in response to cellular stresses and this capability is compromised in the presence of muHtt, causing accumulation of cellular damages overtime and eventually neurodegeneration. Specifically, the following two aims are proposed.

Aim 1: Map the dynamic Htt/muHtt interactome in response to proteotoxic stress by unbiased quantitative proteomic and bioinformatic analyses. We will first establish an ascorbate peroxidase (APEX2)-based proximity labeling platform to spatiotemporally label Htt-interacting proteins in live cells. A striatal STHdhQ7 neuronal cell line stably expressing Htt-APEX2 will be subjected to three different conditions (normal, proteotoxic stress and stress recovery) followed by APEX2 labeling. Biotinylated proteins will then be identified by quantitative proteomics. The resulting protein list will be subjected to in-depth bioinformatic analyses.

Aim 2: Quantitatively analyze the molecular responses of known Htt-interacting proteins to cellular stresses in normal and HD cells. We will focus on analyzing a signaling hub protein, p62, which directly interacts with Htt. Our working hypothesis is that Htt regulates the molecular responses of p62 to cellular stresses and the regulation is impaired in the presence of muHtt. To test this hypothesis, molecular changes of p62 to various stresses in normal and HD cells will be evaluated at (1) mRNA levels, (2) protein expression and (3) subcellular localization. The interaction between p62 and Htt/muHtt under various stresses will be quantified using the Htt-APEX2 platform.

We are well-positioned to undertake the proposed study because our research team consists of uniquely qualified individuals with combined expertise in molecular neurobiology and large data analysis. Successful completion of these studies will contribute fundamental knowledge about molecular functions of normal and mutant Htt and the pathogenesis of HD. In a broader aspect, building dynamic interaction networks under diverse stress conditions could be the key to understand the molecular differences between healthy and any pathological states. The proposed research is highly innovative for its novel idea and approaches to study the dynamic nature of Htt interactome in response to stresses.

 

2. Collaborative Research: A Generalizable Data Framework Towards Precision Radiotherapy

Funder	National Science Foundation (NSF)
NSF Grant #	1918925
Investigators
Co-PI	Jun Deng, PhD | Yale University
Co-PI	Enrico Capobianco, PhD | University of Miami

Abstract

In treating cancer patients with radiation therapy, different patients may have different responses to the same type of radiotherapy. Hence, it is critical to individualize the radiation treatment based on the patient’s health data, clinical conditions, as well as response over time. The goal of the project is to develop a generalizable data framework that can support precision radiotherapy for individual cancer patients. Specifically, a deep reinforcement learning model will be built and validated with multimodal imaging data acquired during diagnosis, treatment and follow-up of individual patients. Harmonization of the medical imaging data with genetic and clinical data will create an invaluable repository of knowledge to draw from, while calling for new analytics. The developed data framework will provide critical clinical decision support for individualized radiotherapy

By leveraging the wealth of data generated in the radiotherapy clinic, the project aims to develop a generalized deep reinforcement learning (DRL) tool for cancer risk stratification. Based on the DRL tool, an ensemble model will be built to analyze all the data types useful to patient outcome prediction. The model will be validated with independent datasets to ensure generalization. To account for information from multiple imaging modalities combined with treatment plans, a multimodal deep reinforcement learning (mDRL) model will be developed and trained with patient data stored in the electronic medical record system, as well as genomic information derived from blood and tissue specimens. The detection tool will be used in both lung cancer and colorectal cancer patients. Generalization to a variety of other cancers will be possible once the tools become available to the clinical research community. The ensemble model will allow integrated analysis of multiple data types recorded along the patient outcome trajectory, provide better discrimination between tumor phenotypes and superior predictive power. The framework will be designed to coordinate and synthesize various types of evidence and measurements into scores for the objective assessment and quantification of outcomes and endpoints. This strategy will ultimately provide novel patient re-stratification and support clinical decisions for highly individualized patient management.